



'''simple docstring'''



import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()
def        UpperCAmelCase (    A	:					Tuple       ,		A	:					List[str]       ,		A	:					Optional[int]					):
							# Initialise PyTorch model
							SCREAMING_SNAKE_CASE       :   int         =      RemBertConfig.from_json_file(A					)
							print('''Building PyTorch model from configuration: {}'''.format(str(A					)					)					)
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      RemBertModel(A					)

							# Load weights from tf checkpoint
							load_tf_weights_in_rembert(A       ,		A       ,		A					)

							# Save pytorch-model
							print('''Save PyTorch model to {}'''.format(A					)					)
							torch.save(model.state_dict()       ,		A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							int             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--rembert_config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained RemBERT model. \n'
							        'This specifies the model architecture.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							lowerCAmelCase_  :							Any             =    parser.parse_args()
							convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)









'''simple docstring'''



def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							return x if y == 0 else greatest_common_divisor(A       ,		x % y					)
def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							return (x * y) // greatest_common_divisor(A       ,		A					)
def        UpperCAmelCase (    A	:					int = 20					):
							SCREAMING_SNAKE_CASE       :   Tuple         =      1
							for i in range(1       ,		n + 1					):
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      lcm(A       ,		A					)
							return g


if __name__ == "__main__":
							print(f'{solution() = }')




